Cloud and AI spend
September’s bill, with your fixes switched on.
An operator reads your cloud bill, finds the waste and who owns it, and drafts the fix. Your team approves before anything goes out.
September bill
$412,800
Illustrative cost export
- rds-reporting-prod$980/mo
- 14 unattached EBS volumes$420/mo
- ml-train node group$2,210/mo
- NAT gateway spike$3,900/mo
- support-summaries tokens$4,600/mo
Switch a fix on to approve it.
With the approved fixes
$412,800
Illustrative arithmetic
Enterprises running operators on Alomana
- up to 30%lower cloud bill
- 15–25%lower AI spend
- up to 92%less time spent on cost allocation
Outcomes reported for cloud and AI cost work like this.
Why waste stays on the bill
A dashboard shows the cost. The operator gets the fix to its owner.
- Nobody owns itFinds the owner from tags, accounts and your team list
- Sized for launch dayDrafts the right size from the usage you share
- Left runningLists idle volumes, snapshots and night-time GPUs
- A spike after a deployFinds the account and the day it started, and drafts a note to the owner
- Tokens nobody budgetedBreaks AI spend down by job and model
- The ticket never got writtenWrites it with the saving and the change, for your yes
How the operator works your cloud bill
Connect
Cost export, Snowflake or PostgreSQL.
Find the waste and the owner
Reads every line and ranks it by saving.
Approve the fix
You say yes. It files the ticket and logs it.
Nothing is sent or filed without a yes.
- Approval before every ticket and message
- Your engineers make the change; the operator writes it up
- Every run logged: input, output, tools, user
Waiting for a yes · illustrative
FIN-418 · NAT gateway · payments account
+$3,900/mo
Route S3 traffic through a gateway endpoint
- Input
- Illustrative cost export · September
- Output
- Route S3 traffic through a gateway endpoint
- Tools
- Sandbox analysis · Jira
- User
- Awaiting approval
- Measured on
- Monthly bill against baseline · Waste found · Fixes shipped · Hours spent chasing owners
- Works with
- Cost and usage exports (CUR, FOCUS) · Snowflake · PostgreSQL · Jira · Slack · Google Sheets · SharePoint · Your cloud APIs via MCP
- Trusted for
- ISO 27001 · GDPR · Never trains on your data · Single-tenant for enterprise
A custom operator typically goes live in up to 4 weeks.
Questions
What is an AI FinOps operator?
An autonomous agent that works your cloud and AI bill. It reads your cost export or billing tables, finds waste, spikes and untagged spend, works out who owns each line, and drafts the fix for your team to approve: a ticket, a message to the owner or a report for finance.
Does it change our cloud resources?
Not on its own. Your engineers make the change; the operator writes it up with the resource, the saving and what to change. Creating a ticket, sending a message or writing to any connected system, including a cloud API connected through MCP, waits for a person's yes.
Which data does it need?
A cost and usage export (AWS CUR or a FOCUS file) is enough to start. Billing data already in Snowflake or PostgreSQL is read through those connectors, and cloud APIs connect through a remote MCP server. Tags, account names and your team list help it find owners.
Which systems does it connect to?
Alomana has 20+ connectors, including Jira, Slack, Snowflake, PostgreSQL, Google Sheets, Google Drive and SharePoint, activated per deployment. Systems outside the library, such as your cloud provider's APIs, connect through a remote MCP server.
How long does it take to deploy?
A custom FinOps operator typically goes live in up to 4 weeks, including validation and integration.
Is our billing and infrastructure data private?
Alomana is ISO 27001 certified and GDPR-compliant, and your data is never used to train AI models. Enterprise deployments run single-tenant, in a dedicated instance.
Bring one month's cost export.
Talk it over with sales: what the operator needs from your systems, and how it goes live.
Alomana is a secure enterprise AI platform where autonomous agents, called operators, run business processes end to end. Every run is fully auditable.